Method and apparatus of data classification for routes in a digitized map

ABSTRACT

A method and system obtaining positioning data from an object traveling on a plurality of routes; mapping the data into a plurality of points on a digital map; identifying points that are matched based on a distance and having a traveling direction consistent with the route directions; obtaining candidate transition points from the plurality of points on the digital map; aggregating the candidate transition points by applying a clustering algorithm to obtain a first cluster of points and a plurality of second clusters of points, verifying a confidence that the first cluster of points are transition points indicating a transition between the routes, and in response to the confidence being below a threshold confidence, classifying the first cluster of points as a first plurality of traveling points having a first direction and automatically adjusting the digital map at least in part based on the first cluster of points.

BACKGROUND

This disclosure is directed to data classification for routes in adigitized map and more particularly to automatically adjusting thedigital map based on the data classification.

A positioning device carried on an object, such as a vehicle orpedestrian, can be used to sense positioning data indicating locationsof the object. The positioning data can be mapped on a digital map aspoints. In the case that map contains information for routes, the pointscan be matched to one or more routes on the map. In this way, by sensingthe positioning data over time, a trajectory of the object can beobtained and traced. Trajectory tracking has been widely used in manysituations such as fleet management, map maintenance, or the like. As anexample, it is possible to implement real-time bus monitoring, real-timebus arrival alerting, and the like. For instance, in the real-time busmonitoring, operation status of a bus may be monitored in real timebased on the trajectory tracking of the bus. As another example, theaccuracy of route information map can be detected by trajectorytracking. In trajectory tracking, an important and fundamental task isto accurately identify “transition points” which indicate thetransitions between routes.

SUMMARY

Example embodiments of the present disclosure provide a method, adevice, and a computer program product for classifying points on a map.

In an aspect, a computer-implemented method is provided. Acomputer-implemented method comprising obtaining positioning data from apositioning device carried on each of a respective at least one objecttraveling on a plurality of routes; mapping the positioning data into aplurality of points on a digital map, the digital map including at leasttwo of the plurality of routes with different route directions, eachpoint of the plurality of points having a traveling direction indicatedby the positioning data, the traveling direction for each point of theplurality of points being saved as metadata; identifying points of theplurality of points on the digital map that are matched to the at leasttwo of the plurality of routes based on a distance separating each pointfrom the at least two of the plurality of routes being less than a firstthreshold and having a traveling direction consistent with the routedirections of the at least two plurality of routes; obtaining candidatetransition points from the plurality of points on the digital map, thecandidate transition points including at least one of the followingpoints that are separated from the at least two of the plurality ofroutes by distances exceeding a second threshold distance, or pointsthat are located between the at least two of the plurality of routes andhave a traveling direction inconsistent with the route directions of theat least two plurality of routes; aggregating the candidate transitionpoints by applying a clustering algorithm to obtain a first cluster ofpoints and a plurality of second clusters of points, the clusters ofpoints being obtained from a plurality of the objects traveling on theplurality of routes or a single object traveling on the plurality ofroutes multiple times; verifying a confidence that the first cluster ofpoints are transition points indicating a transition between the routes,at least in part based on distances between the first cluster of pointsand the plurality of second clusters of points; and in response to theconfidence being below a threshold confidence, classifying the firstcluster of points as a first plurality of traveling points having afirst direction and automatically adjusting the digital map at least inpart based on the first cluster of points

In another aspect, a device is provided. A device comprising aprocessing unit; a memory coupled to the processing unit and storinginstructions thereon, the instructions, when executed by the processingunit, performing acts including obtaining positioning data from apositioning device carried on each of a respective at least one objecttraveling on a plurality of routes; mapping the positioning data into aplurality of points on a digital map, the digital map including at leasttwo of the plurality of routes with different route directions, eachpoint of the plurality of points having a traveling direction indicatedby the positioning data, the traveling direction for each point of theplurality of points being saved as metadata; identifying points of theplurality of points on the digital map that are matched to the at leasttwo of the plurality of routes based on a distance separating each pointfrom the at least two of the plurality of routes being less than a firstthreshold and having a traveling direction consistent with the routedirections of the at least two plurality of routes; obtaining candidatetransition points from the plurality of points on the digital map, thecandidate transition points including at least one of the followingpoints that are separated from the at least two of the plurality ofroutes by distances exceeding a second threshold distance, or pointsthat are located between the at least two of the plurality of routes andhave a traveling direction inconsistent with the route directions of theat least two plurality of routes; aggregating the candidate transitionpoints by applying a clustering algorithm to obtain a first cluster ofpoints and a plurality of second clusters of points, the clusters ofpoints being obtained from a plurality of the objects traveling on theplurality of routes or a single object traveling on the plurality ofroutes multiple times; verifying a confidence that the first cluster ofpoints are transition points indicating a transition between the routes,at least in part based on distances between the first cluster of pointsand the plurality of second clusters of points; and in response to theconfidence being below a threshold confidence, classifying the firstcluster of points as a first plurality of traveling points having afirst direction and automatically adjusting the digital map at least inpart based on the first cluster of points.

In yet another aspect, a computer program product is provided. Acomputer program product being tangibly stored on a non-transientmachine-readable medium and comprising machine-executable instructions,the instructions, when executed on a device, causing the device toobtain positioning data from a positioning device carried on each of arespective at least one object traveling on a plurality of routes; mapthe positioning data into a plurality of points on a digital map, thedigital map including at least two of the plurality of routes withdifferent route directions, each point of the plurality of points havinga traveling direction indicated by the positioning data, the travelingdirection for each point of the plurality of points being saved asmetadata; identify points of the plurality of points on the digital mapthat are matched to the at least two of the plurality of routes based ona distance separating each point from the at least two of the pluralityof routes being less than a first threshold and having a travelingdirection consistent with the route directions of the at least twoplurality of routes; obtain candidate transition points from theplurality of points on the digital map, the candidate transition pointsincluding at least one of the following points that are separated fromthe at least two of the plurality of routes by distances exceeding asecond threshold distance, or points that are located between the atleast two of the plurality of routes and have a traveling directioninconsistent with the route directions of the at least two plurality ofroutes; aggregate the candidate transition points by applying aclustering algorithm to obtain a first cluster of points and a pluralityof second clusters of points, the clusters of points being obtained froma plurality of the objects traveling on the plurality of routes or asingle object traveling on the plurality of routes multiple times;verify a confidence that the first cluster of points are transitionpoints indicating a transition between the routes, at least in partbased on distances between the first cluster of points and the pluralityof second clusters of points; and in response to the confidence beingbelow a threshold confidence, classify the first cluster of points as afirst plurality of traveling points having a first direction andautomatically adjusting the digital map at least in part based on thefirst cluster of points.

It is to be understood that the Summary is not intended to identify keyor essential features of embodiments of the present disclosure, nor isit intended to be used to limit the scope of the present disclosure.Other features of the present disclosure will become easilycomprehensible through the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of some embodiments of the presentdisclosure in the accompanying drawings, the above and other objects,features and advantages of the present disclosure will become moreapparent, wherein the same reference generally refers to the samecomponents in the embodiments of the present disclosure.

FIG. 1 is a block diagram of an electronic device suitable forimplementing embodiments of the present disclosure;

FIG. 2 is an example map to which embodiments of the present disclosureare applicable;

FIG. 3 is a flowchart of an example method for classifying points on amap in accordance with some embodiments of the present disclosure; and

FIG. 4 is a further example map to which embodiments of the presentdisclosure are applicable.

FIG. 5 is a block diagram of one embodiment of a processing apparatus inaccordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

Principle of the present disclosure will now be described with referenceto some example embodiments. It is to be understood that theseembodiments are described only for the purpose of illustration and helpthose skilled in the art to understand and implement the presentdisclosure, without suggesting any limitations as to the scope of thedisclosure. The disclosure described herein can be implemented invarious manners other than the ones describe below.

As used herein, the term “includes” and its variants are to be read asopen terms that mean “includes, but is not limited to.” The term “basedon” is to be read as “based at least in part on.” The term “oneembodiment” and “an embodiment” are to be read as “at least oneembodiment.” The term “another embodiment” is to be read as “at leastone other embodiment.” Other definitions, explicit and implicit, may beincluded below.

Reference is first made to FIG. 1, in which an exemplary electronicdevice or computer system/server 12 which is applicable to implement theembodiments of the present disclosure is shown. Computer system/server12 is only illustrative and is not intended to suggest any limitation asto the scope of use or functionality of embodiments of the disclosuredescribed herein.

As shown in FIG. 1, computer system/server 12 is shown in the form of ageneral-purpose computing device. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the disclosure.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the disclosure as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, and thelike. One or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via input/output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, and thelike.

In computer system/server 12, I/O interfaces 22 may support one or moreof various different input devices that can be used to provide input tocomputer system/server 12. For example, the input device(s) may includea user device such keyboard, keypad, touch pad, trackball, and the like.The input device(s) may implement one or more natural user interfacetechniques, such as speech recognition, touch and stylus recognition,recognition of gestures in contact with the input device(s) and adjacentto the input device(s), recognition of air gestures, head and eyetracking, voice and speech recognition, sensing user brain activity, andmachine intelligence.

As described above, in trajectory tracking, the fundamental task is tocorrectly and accurately identify the transition points. The transitionpoints are corresponding to one or more transitions between the routes.As used herein, a transition refers to a location or a travel distancewhere an entity transits between routes having different routedirections on the map. That is, a transition point is located betweenthe end of one route and the beginning of another route. In order toobtain updating information on the routes on the map to achieveeffective and efficient fleet management, the conventional approachesdepend upon manual labor for observations and measurements by humanusers. For example, a vehicle provided with the positioning device mayperiodically travel along roads to detect changes associated with theroads. If a change of a road is found, the corresponding route on themap may be adjusted accordingly. Then, the points on the map may bematched with the changed route. However, such a manual update andmaintenance of the routes on the map is inefficiency in terms of bothtime and cost especially when the roads changes frequently, for example,due to needs of city development, city constructions, and the like.

In order to address the above and other potential problems, embodimentsof the present disclosure provide an effective and efficient solutionfor identifying transition points on the digital map. Generallyspeaking, the proposed solution works on the basis of category of pointson the map, which will be explained with reference to FIG. 2.

As described above, positioning data sensed by the positioning devicecarried on the object can be mapped into points on the digital map. Thenthe mapped points of the object can be matched to one or more routes onthe map. Each point has a traveling direction which can be indicated bythe positioning data, for example. The traveling directions for thepoints on the map can be saved as metadata. The points on the map can bematched to the routes based on the distances between the points and theroutes as well as the traveling directions of the points and the routedirections. More particularly, if the distance between a point and aroute is short enough and the traveling direction of the point isconsistent with the direction of the route, then the point is matched tothat route. In the context of the present disclosure, such a point isreferred to a “matched point” or a “traveling point” which indicatesthat the respective object is moving along the route.

In FIG. 2, the points shown by solid circles such as the point 206 aretraveling points that are matched to the route 210 or 220. In thisexample, the routes 210 and 220 have different directions 203 and 204,respectively. It can be seen that in the map matching, a certain degreeof tolerance is given. More particularly, although the point 206 isseparated from the route 220, it can be considered as a traveling pointbecause its distance to the route 220 is below the threshold distance(referred to as “first threshold distance”).

The points on the map other than the matched points are referred to“unmatched points.” It would be appreciated that the unmatched pointsmay include points that are separated from a route by distancesexceeding a predefined threshold distance, such as points 208 as shownin FIG. 2. Alternatively, or in addition, the unmatched points mayinclude points with directions inconsistent with the route directions,such as points 207.

According to embodiments of the present disclosure, potential orcandidate transition points are identified from those unmatched points.Then the confidence of these candidate transition points will beverified to check whether they actually represent transition points onethe map. If one or more candidate transition points are determined tohave low confidence, it means that these candidate transition points,such as the point 208 in FIG. 2, are not real transition points. In someembodiments, such points can be classified as traveling points. However,as mentioned above, these points are unmatched with any routes on themap. As a result, it can be determined that some problem occurs such asthe map data is not up-to-date and/or the respective entities that aresensing the positioning data are not traveling along the predeterminedroutes. On the other hand, the candidate transition points with highconfidence, such as the point 207 in FIG. 2, are be classified as realtransition points, as will be described below.

To this end, according to embodiments of the present disclosure, theunmatched points are categorized as the following three types of points:(1) points that are separated from the routes by the distances exceedingthe first threshold distance, such as the point 208; (2) points that arelocated between the routes but inconsistent with the route directions,such as the point 207; and (3) points that are located on one of theroutes but have directions inconsistent with the respective routedirections.

Among these types of points, the points of the first type indicate thatthe object is leaving the routes. The points of the second type arereferred to as “transition points” which represent a transition betweenthe routes. The points of third type indicate that the object stopsmoving along the routes which are referred to as “stopping points”representing a stop along the routes. According to embodiments, theunmatched points of the first or second type will be selected ascandidate transition points. By verifying the confidence, these twotypes of points can be separated from each other, such that theunmatched points having high confidence, such as the clusters of points201 and 202 are classified as the real transition points. The existenceof unmatched points like the point 208 (if any) may indicate a problemto be handled. For example, the map data may be out of date, or theentity may be traveling out of the predefined routes. Additionally, insome embodiments, the points of the third type may be assigned with adirection and classified as special traveling points.

In the example shown in FIG. 2, it is assumed that the routes 210 and220 together form a bus line. Accordingly, points on the map 200 can begenerated based on positioning data acquired by one or more busesequipped with positioning devices. As shown, the points 205 and 206 aretraveling points whose directions are consistent with the routedirection 203 and 204, respectively. The unmatched points such as points207 and 208 are separated from the routes 210 and 220 by the distancesexceeding the first threshold distance and/or inconsistent with theroute directions 203 and 204. The unmatched points 207 may be caused dueto following events: the bus stops moving due to traffic lights, trafficcongestion and the like; the bus leaves the bus line for reasons such asfuel-up, relaxation, and the like; and the bus line has been changed. Inthe shown example, a route change from a part 221 of the route 220 tothe route 222 occurs. That is, in practice the bus has changed to travelalong the route 222 instead of the part 221 of the route 220. However,the map 200 has not yet been updated accordingly. In this case, thetrajectory of the bus is still traced by matching the points on the mapwith the part 221 of the route 220. As a result, the point 208 isunmatched with any routes. Such kind of problem may significantlydegrade the accuracy of the trajectory trace of the bus.

By detecting transition points according to embodiments of the presentdisclosure, the above problems can be identified and handled. It is tobe understood that the example scenario of the bus trace is describedabove only for the purpose of illustration, without suggesting anylimitations as to the scope of the disclosure. Embodiments of thepresent disclosure may be implemented in any other suitable applicationwhere a trajectory of an object needs to be traced.

FIG. 3 shows a flowchart of an example method 300 for classifying thepoints on the map in accordance with some embodiments of the presentdisclosure. The method 300 may be used to process the points on the map200 illustrated in FIG. 2. As described above, the points on the map areobtained by mapping the positioning data acquired by one or moreentities. As used herein, the term “entity” refers to any object ordevice whose location can be sensed. Examples of an entity include, butare not limited to, a motor vehicle, rickshaw, pedestrian, and the like.In particular, in the context of the present disclosure, when the samevehicle or pedestrian travels along the same route at different time,the vehicle or pedestrian will be regarded as two different entities.

An entity may be equipped with a location sensing device, such as aGlobal Navigation Satellite System (GNSS) device. Examples of GNSSinclude, but not limited to, one or more of the following: GlobalPositioning System (GPS), Galileo positioning system, Beidou navigationpositioning system, and the like. With the location sensing device, eachentity may obtain positioning data of a plurality of locations duringthe traveling process. In one embodiment, the positioning data, forexample, may be altitude and latitude data. Optionally, the positioningdata may also comprise velocity data and direction data and the like ofthe entity at a corresponding location

In step 302, candidate transition points on the map 200 are obtained.The candidate transition points includes points that are separated fromthe routes 210 and 220 by distances exceeding the first thresholddistance, and/or points that are located between the routes 210 and 220but inconsistent with the route directions 203 and 204.

As described above, in the case that a change associated with the actualroad occurs, if the corresponding route on the map is not timelychanged, the unmatched points occur. The candidate transition points canbe determined from the unmatched points. For example, in someembodiments, the following unmatched points can be selected as thecandidate transition points: points that are separated from the routes210 and 220 by the distances exceeding the first threshold distance,and/or points that are inconsistent with the route directions 203 and204. To this end, in some embodiments, the candidate transition pointscan be obtained in step 302 by first determining unmatched points on themap and then excluding those unmatched points located on the routes buthaving directions inconsistent with the respective route directions.That is, the unmatched points of the third type (stopping points) asdescribed above are excluded, and the remaining unmatched points of thefirst and second types are used as candidate transition points.

Then, in step 304, the candidate transition points are aggregated toobtain a first cluster of points and a plurality of second clusters ofpoints. The candidate transition points may be aggregated according tomany factors. The aggregation may be implemented by clusteringalgorithms, either currently known or to be developed in the future, orany other approaches. In some embodiments, distances between thecandidate transition points may be used for point aggregation. Forexample, the candidate transition points having short distancestherebetween may be aggregated together. In such embodiments, if adistance between two candidate transition points is below a thresholddistance, the two candidate transition points may be aggregated into onecluster of points. The threshold distance for the aggregating may be setas any suitable distance according to practical requirements.Alternatively, or in addition, in some embodiments, the aggregation ofthe candidate transition points may be further based on the acquisitiontime of the associated positioning data. For example, if a sequence ofpositioning data, which correspond to some candidate transition points,is acquired sequentially in a predefined time interval, these candidatetransition points may be aggregated together.

After the candidate transition points are aggregated into a plurality ofclusters of points, the first and second clusters of points may beselected from these clusters of points. For example, the first clusterof points may be generated based on positioning data acquired by a firstentity, and the second clusters of points may be generated based onpositioning data acquired by one or more second entities different fromthe first entity. As an alternative example, the first and secondclusters of points may be generated based on the positioning dataacquired by one entity when traveling along the routes many times.

Next, the method 300 proceeds to step 306, where a confidence of thefirst cluster of points is verified. As used herein, the term“confidence” refers to the possibility that a cluster of points indeedrepresents a transition indicating the entity is transiting from oneroute to another route. A high confidence indicates that the cluster ofpoints is more likely to be the transition points, and vice versa.According to embodiments, the confidence is verified at least in partbased on distances between the first cluster of points and the secondclusters of points. To this end, various approaches can be used todetermine the distance between the first and second clusters of points.

For example, in some embodiments, the Euclidean distance betweengeometric centers of the first and second clusters of points may becalculated as the distance. The geometric center of a cluster of pointsmay be determined in a variety of ways. For example, in one embodiment,it is possible to determine a bounding box of the respective cluster ofpoints and then use the center of the bounding box as the geometriccenter. Alternatively, a shape defined by a boundary of the cluster ofpoints can be determined and the symmetric center of the shape can beused as the geometric center. As another example, the distance betweenthe first and second clusters of points may be calculated based on adistance between two reference points in the two clusters of points.

The distances between the first cluster of points and the secondclusters of points may be used in a variety of manners to verify theconfidence of the first cluster of points. In some embodiments, theconfidence of the first cluster of points may be verified based on anumber of proximate clusters of points with respect to the first clusterof points from among the second clusters of points. For example, aproximate cluster of points will be selected from among the plurality ofsecond clusters of points if the distance between the first cluster ofpoints and the proximate cluster of points is below a threshold distance(referred to as “second threshold distance”). The second thresholddistance for the verification may be set according to practicalrequirements or any other relevant factors. Then, the selected proximateclusters of points are counted. If the number of the proximate clustersof points exceeds a threshold number, the confidence of the firstcluster of points may be determined to be high. This indicates that itis quite possible that the first cluster of points indeed represents atransition between the routes.

Otherwise, if the number of the proximate clusters of points is belowthe threshold number, the confidence of the first cluster of points isdetermined low. This indicates that the first cluster of points does notrepresent a transition between the routes.

In addition to the distances between the first and second clusters ofpoints, the confidence of the first cluster of points may be verifiedfurther based on the comparison of travel distances between twopositions along different routes. More particularly, as shown in FIG. 2,the routes 202 and 203 are paired to form a bus line and have oppositedirections. A predetermined reference transition may be used as alandmark. The predetermined reference transition represents thetransition between the routes with opposite directions. Then a firsttravel distance from the first cluster of points to the predeterminedreference transition along the route 202 may be determined.Additionally, a second travel distance from the predetermined referencetransition to the first cluster of points along the route 203 may bedetermined. The first and second travel distances are compared to oneanother. If their difference is below a threshold difference, then itcan be considered that confidence for the first cluster of points ishigh enough, that is, above the threshold confidence. Otherwise, theconfidence may be determined to be low. Such embodiments work on thebasis of an observation that the travel distances along two pairedroutes with opposite directions are often similar. Accordingly, thereference transition zone may be any suitable transition between routeshaving nearly equal distances to and from the first cluster of points.

Alternatively, or in addition, the confidence of the first cluster ofpoints may be verified based on reference information obtained from auser input. For example, a user may specify the confidence of the firstcluster of points, for example, based on known information on associatedroute changes. As an alternative example, the routes may be investigatedor measured on the spot to determine whether the first cluster of pointsindeed represents a transition. Then, the investigations may be input bythe user. The verification of the confidence based on the user input mayfurther increase accuracy of the verification.

In the case that the confidence is determined to be below a thresholdconfidence, the method 300 proceeds to step 308, where the first clusterof points is classified to be traveling points. For the purpose ofdiscussion, the classified traveling points will be referred to as firsttraveling points having a first direction. If the confidence isdetermined to exceed the threshold confidence, the first cluster ofpoints is classified as the transition points.

In some embodiments, the first direction may be determined based ondirections of proximate traveling points with respect to the firstcluster of points. These proximate traveling points can be referred toas a second plurality of traveling points. For example, the firstdirection of the first cluster of points may be determined to be adominant direction of the proximate traveling points. In the context ofthe present disclosure, a dominant direction refers to a direction ofthe majority points in a set of points.

In one embodiment, a set of proximate traveling points are obtainedbased on the distances between these traveling points and the firstcluster of points. The distances between the proximate traveling pointsand the first cluster of points are below a threshold distance (asreferred to as “third threshold distance”). If the number of thetraveling points having a specific direction from among of the proximatetraveling points exceeds a threshold number, the specific direction maybe determined as the dominant direction of the proximate travelingpoints.

Alternatively, in some other embodiments, the first direction of thefirst cluster of points may be determined based on a user input in orderto ensure the accuracy of the classification. For example, the user mayspecify the direction of the first cluster of points based on knowninformation and on-the-spot investigations or measurements on associatedroute changes.

Through the above discussions, it would be appreciated that inaccordance with embodiments of the present disclosure, a confidence of acluster of points from among candidate transition points may beverified. In response to a low confidence, the cluster of points may beclassified to traveling points. In this way, the accuracy of thetrajectory tracking may be improved in more effective and efficient waywhile reducing manpower and material costs.

As mentioned above, if the first cluster of points is classified to thetraveling points, it means that the route information of the map mighthave some errors or be out of date. In this situation, in oneembodiment, the route on the map may be adjusted at least in part basedon the first classified cluster of points. For example, it may benecessary to add a new route(s) and/or modify an existing route(s). Anyroute adjustment approaches can be used in connection with embodimentsof the present disclosure. It would be appreciated that such routeadjusting approach is more effective and efficient compared with theconventional manual way.

In addition to the classification of the candidate transition points,other points such as stopping points and traveling points may also beclassified as will be discussed in detail in the following paragraphs.For example, as described above, if an unmatched point is located on aroute but has traveling direction inconsistent with the route direction,that point is considered as a stopping point. In some embodiments,depending on the requirements, it is possible to classify the stoppingpoint as a traveling point and assign it with the route direction.Example processes of classifying the points on the map will now bedescribed below with reference to with reference to FIG. 4.

FIG. 4 shows an example map 400 to which embodiments of the presentdisclosure are applicable. The map 400 can be considered as a furtherexample implementation of the map 200. The map 400 differs from the map200 as described above in that some unmatched points 207, as indicatedby triangles, on the map 400 have been aggregated into clusters ofpoints 401 and 402.

Similar to the aggregation of the candidate transition points asdescribed above, the unmatched points may be aggregated according tomany factors. In this example, the unmatched points 207 are aggregatedbased on the distances between the unmatched points 207 and theacquisition time of associated positioning data. As shown, the clusterof points 401 is obtained by aggregating the unmatched points that areseparated from the routes 210 and 220 by the distances exceeding thefirst threshold distance, that is, the candidate points. Accordingly,the confidence of the cluster of points 401 is verified. It is to beunderstood that the processes of the verification as described abovewith reference to FIG. 3 can also be implemented for the cluster ofpoints 401, and the details thereof will be omitted.

In this example, the cluster of points 401 is determined to have a lowconfidence, which is caused due to the route change from the part 221 ofthe route 220 to the route 222. Then, the cluster of points 401 may beclassified as traveling points. As shown, most of the traveling pointsproximate to the cluster of points 401 have the route direction 204.That is, the dominant direction of the traveling points proximate to thecluster of points 401 is route direction 204. As a result, the clusterof points 401 is classified as the traveling points having the routedirection 204.

After the cluster of points 401 is classified, from among the travelingpoints proximate to the cluster of points 401, a traveling point havinga direction other than the dominant direction may also be classifiedbased on the dominant direction. More particularly, if the direction oftraveling point is different from the dominant direction of theproximate traveling point, then it is possible directly assign thedominant direction to this traveling point. In this example, a set oftraveling points 403 have the route direction 203. However, the dominantdirection of the traveling points proximate to the set of travelingpoints 403 is consistent with the route direction 204. As a result, insome embodiments, the directions of the set of traveling points 403 areadjusted to be the route direction 204.

As shown, the cluster of points 402 are obtained by aggregating theunmatched points that are located on the route 210 but inconsistent withthe route direction 203, namely, the stopping points representing a stopalong the route 210. Due to sensing characteristics of a locationsensing device, the points proximate to the stopping points typicallyhave directions that are frequently changed. Accordingly, in thisexample, in addition to the stopping points, the cluster of points 402might also include some nearby traveling points. That is, in thisexample, in aggregating the stopping points into the cluster of points402, some traveling points are also aggregated together due to thesetraveling points being proximate to the stopping points in terms of bothlocations and time.

Then, the cluster of points 402 may also be classified to travelingpoints. The direction of the stopping points may be determined as theroute direction 203 of the located route 210.

Automatically maintaining accurate digitalized routes is critical forthe applications in fleet management, such as bus arrival predication,trip planning in online maps, vehicle monitoring, etc. Automaticallyupdating digitalized routes is a task to extract n routes from raw GPSdata (usually n=2). The data of different driving directions must beseparate from each other before extraction. However, raw GPS data ofdifferent routes and directions are usually mixed together without anyother advanced fleet management sensor system.

Digitalized routes data typically is a sequence of GPS pointsrepresenting the routes that the vehicles are actually driving on. Avehicle's accurate location and driving distance can be calculated bymatching a vehicle's current GPS location to a location along the route.Manually collecting and maintaining this information can be atime-consuming and costly task. For example, there are usually thousandsof routes for a city, and each routes contains hundreds to thousands ofGPS points (500,000˜1000,000 points for a city). In addition, theseroutes are always changing, due to the needs of city development,constructions, new metro stations, etc. 7%-20% routes would be changedper month. As a results, 12%-39% of the route information (GPSlocations) are in error or out of date.

Digitalized route data is critical to many applications of fleetmanagement, especially in the age of the connected vehicle. Drivingdirections is critical information for automatic route extraction fromraw GPS data. Thus, GPS data classification, which infers the drivingdirections, is a prerequisite.

Vehicles in a fleet usually have N operational routes. In most casesN=2, based on driving from location A to location B, and then back.Ideally vehicles may have the following status in operation hours:

-   -   1. Driving on route 1;    -   2. Driving on route 2;    -   3. Stop at location A or location B (transition status).

The goal is for the GPS data points logged from vehicles to beclassified into three classes, which can be labeled with 0 (on route 1),1 (on route 2), −1 (not on route 1 or 2).

However, in practical operation conditions, there are many exceptions:

a) GPS/3G/4G signals are unstable. They are easily affected by manyfactors, e.g.: weather conditions, high buildings, elevated roads,tunnels, bridges . . . . So GPS data have many errors.

Due to some other operation needs, like maintenance, refueling, dynamicdispatching etc., there are usually large numbers of noisy trips amountGPS trace data, which are very different from normal traces (driving onroute 1 or 2). 3. Existing digitalized routes data is out-of-date. SoGPS data points cannot match to any of routes.

Traffic congestions cause the GPS data points stay at one location for along time (so it may looks like vehicle in transition status).

This is a problem the disclosed invention solves. With the disclosedinvention, digitalized routes can be fixed automatically based on onlyraw GPS data (when they are out-of-date). With the disclosed invention,thousands of routes for a city can be auto-generated based on latest 3-6days historical GPS data, within 10 hours on normal PC.

FIG. 5 is an example of one embodiment of an apparatus 500 of GPS dataclassification for digitalized routes. One or more features of theapparatus 500 could (without limitation) be implemented as a computerprogram product embodying one or more program modules 42 (FIG. 1) storedin memory 28 (FIG. 1). As shown in FIG. 5, raw data 502 (vehicle id,locations, timestamps, speeds, driving directions, etc.) from GPSsatellites 504 obtained GPS devices on a large number of vehicles 506from a fleet is obtained. The raw GPS data is cleaned for processingusing well known signal processing techniques by data cleaning device508. The cleaned GPS data 510 is input to Initial Classifier processingmodule 512. Digitalized routes data 514 that can be inaccurate orout-of-date is also input to classifier module 512. The output isclassified GPS data for different routes in which each data point islabeled with route id, or −1, with no label. In fleet management, thereare usually preliminary classification results within raw GPS data.Because the task of vehicle monitoring will locate each vehicle (whichroute the vehicle is driving on). Many existing methods such as HiddenMarkov model, dynamic planning or curve matching, can serve as thispurpose.

The initially classified GPS data is input to Micro-cluster SequenceBuilder module 516. Module 516 identifies the unmatched points andobtains the candidate transition points as described in connection withstep 302 of FIG. 3. Module 516 transfers point sequences in the GPS datainto micro-cluster sequences. In one embodiment, adjacent GPS pointswith same label can be merged. One micro-cluster only has one label: −1,0, 1, . . . , n−1 (n-classification, usually n=2); −1 micro-cluster fallinto 2 categories: stopping and transition. Module 516 also identifiesthe stopping points. Module 516 determines transition micro-clusters.

Module 516 outputs the micro-cluster data 518 to Homogeneous Mergemodule 520. Module 520 merges three micro-clusters: two same labelmicro-clusters with one stopping micro-clusters in the middle.Transition micro-clusters in middle cannot be merged.

Transition micro-clusters are then input to Transition micro-clustersclustering module 522. Module 522 performs a clustering method, such ashierarchy clustering on all transition micro-clusters from differentvehicles. As noted above, in one embodiment, the distance between twomicro-clusters is the Euclidean distance between their centroids. Module522 aggregates candidate transition points as described in connectionwith step 304.

Real transition micro-clusters detector module 524 identifies n realtransition micro-clusters as described in connection with step 306. Inone embodiment, module 524 considers transition frequency and balance ofdistance in two half portions of the routes.

The n-real transition micro-clusters are then input to Label calibrationand merge module 526. Optionally, other source reference data 528, suchas user data, are included to enhance the confidence in theidentification of the real transition micro-clusters. Module 526calibrates the labels for all micro-clusters and classifies themicro-clusters as described in connection with step 308. Module 526merges adjacent micro-clusters of the same direction. In one embodiment,the labels are generated based on the fact that the label can onlychange at real transition points in the sequence.

The present disclosure may be a system, an apparatus, a device, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to pedestrianize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present disclosure. In this regard, each block in the flowchartor block diagrams may represent a module, snippet, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reversed order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for tracking the route trajectory of a traveling object comprising: obtaining positioning data from a positioning device carried on each of a respective at least one traveling object traveling on a plurality of routes; mapping the positioning data into a plurality of points on a digital map, the digital map including a stored route trajectory along at least two of the plurality of routes, the at least two plurality of routes having different route directions, each point of the plurality of points having a traveling direction indicated by the positioning data, the traveling direction for each point of the plurality of points being saved as metadata; identifying points of the plurality of points on the digital map that are matched to the stored route trajectory along the at least two of the plurality of routes based on a distance separating each point from the stored trajectory along the at least two of the plurality of routes being less than a first threshold and having a traveling direction consistent with the route directions of the stored trajectory along the at least two plurality of routes; obtaining candidate transition points from the plurality of points on the digital map, the candidate transition points being points that are unmatched to the stored route trajectory along the at least two of the plurality of routes; aggregating a plurality of candidate transition points by applying a clustering algorithm to obtain a first cluster of points and a plurality of second clusters of points, the clusters of points being obtained from one of positioning data obtained from a plurality of the traveling objects traveling on the plurality of routes or positioning data obtained from a single object traveling on the plurality of routes multiple times; classifying the first cluster of points as transition points between the at least two plurality of routes or travelling points on one of the plurality of routes, based on verifying a confidence level, the confidence level being verified at least in part based on distances between the first cluster of points and the plurality of second clusters of points; wherein in response to verifying that the confidence level is below a threshold confidence, classifying the first cluster of points as a first plurality of traveling points having a first direction, and in response to verifying that the confidence level is above the threshold confidence, classifying the first cluster of points as the transition points; and automatically adjusting the stored route trajectory at least in part based on the classification of the first cluster of points.
 2. The method of claim 1, wherein the verifying comprises: selecting, from among the plurality of second clusters of points, clusters of points such that a distance between each of the selected clusters of points and the first cluster of points is below a second threshold distance; and in response to the number of the selected clusters of points being below a threshold number, determining that the confidence is below the threshold confidence.
 3. The method of claim 1, wherein the at least two plurality of routes at least include a first route and a second route having opposite directions, and wherein the verifying comprises: determining a first travel distance from a first cluster of points to a predetermined reference transition along the first route, the predetermined reference transition representing a transition between the first and second routes; determining a second travel distance from the predetermined reference transition to the first cluster of points along the second route; determining a difference between the first and second travel distances; and in response to the difference being below a threshold difference, determining that the confidence exceeds the threshold confidence.
 4. The method of claim 1, wherein the classifying comprises: determining the first direction for the first plurality of traveling points by: determining a dominant direction of a second plurality of traveling points with respect to the first cluster of points, a distance between each of the second plurality of traveling points and the first cluster of points being below a third threshold distance, the second plurality of traveling points representing movements along the plurality of routes; and determining the first direction at least in part based on the dominant direction of the second plurality of traveling points.
 5. The method of claim 4, further comprising: selecting, from among the second plurality of traveling points, a traveling point having a second direction other than the dominant direction; and adjusting the second direction of the selected traveling point based on the dominant direction.
 6. The method of claim 1, wherein the unmatched points include at least one of the following: points that are separated from the at least two of the plurality of routes by distances exceeding the first threshold distance, or points having directions that are inconsistent with the route directions; and selecting the candidate transition points from the unmatched points.
 7. The method of claim 6, further comprising: in response to determining that one of the unmatched points is located on one of the at least two plurality of routes, classifying the unmatched point as a traveling point having the route direction of the located route.
 8. A device for tracking the route trajectory of a traveling object comprising: a processing unit; a memory coupled to the processing unit and storing instructions thereon, the instructions, when executed by the processing unit, causing the device to perform: obtaining positioning data from a positioning device carried on each of a respective at least one traveling object traveling on a plurality of routes; mapping the positioning data into a plurality of points on a digital map, the digital map including a stored route trajectory along at least two of the plurality of routes, the at least two plurality of routes having different route directions, each point of the plurality of points having a traveling direction indicated by the positioning data, the traveling direction for each point of the plurality of points being saved as metadata; identifying points of the plurality of points on the digital map that are matched to the stored route trajectory along the at least two of the plurality of routes based on a distance separating each point from the stored trajectory along the at least two of the plurality of routes being less than a first threshold and having a traveling direction consistent with the route directions of the stored trajectory along the at least two plurality of routes; obtaining candidate transition points from the plurality of points on the digital map, the candidate transition points being points that are unmatched to the stored route trajectory along the at least two of the plurality of routes aggregating a plurality of candidate transition points by applying a clustering algorithm to obtain a first cluster of points and a plurality of second clusters of points, the clusters of points being obtained from one of positioning data obtained from a plurality of the traveling objects traveling on the plurality of routes or positioning data obtained from a single object traveling on the plurality of routes multiple times; classifying the first cluster of points as transition points between the at least two plurality of routes or travelling points on one of the plurality of routes, based on verifying a confidence level, the confidence level being verified at least in part based on distances between the first cluster of points and the plurality of second clusters of points; wherein in response to verifying that the confidence level is below a threshold confidence, classifying the first cluster of points as a first plurality of traveling points having a first direction, and in response to verifying that the confidence level is above the threshold confidence, classifying the first cluster of points as the transition points; and automatically adjusting the stored route trajectory at least in part based on the classification of the first cluster of points.
 9. The device of claim 8, wherein the verifying comprises: selecting, from among the plurality of second clusters of points, clusters of points such that a distance between each of the selected clusters of points and the first cluster of points is below a second threshold distance; and in response to the number of the selected clusters of points being below a threshold number, determining that the confidence is below the threshold confidence.
 10. The device of claim 8, wherein the at least two plurality of routes at least include a first route and a second route having opposite directions, and wherein the verifying comprises: determining a first travel distance from a first cluster of points to a predetermined reference transition along the first route, the predetermined reference transition representing a transition between the first and second routes; determining a second travel distance from the predetermined reference transition to the first cluster of points along the second route; determining a difference between the first and second travel distances; and in response to the difference being below a threshold difference, determining that the confidence exceeds the threshold confidence.
 11. The device of claim 8, wherein the instructions further cause the device to: adjusting the stored route trajectory on the map at least in part based on the classified first cluster of points.
 12. The device of claim 8, further comprising a transition micro-clusters clustering module for aggregating the candidate transition points, a real transition micro-clusters detector module for verifying a confidence in identifying clusters of points as transition points and a label calibration and merge module for classifying the cluster of points.
 13. The device of claim 8, wherein the classifying comprises: determining the first direction for the first plurality of traveling points by: determining a dominant direction of a second plurality of traveling points with respect to the first cluster of points, a distance between each of the second plurality of traveling points and the first cluster of points being below a third threshold distance, the second plurality of traveling points representing movements along the plurality of routes; and determining the first direction at least in part based on the dominant direction of the second plurality of traveling points.
 14. The device of claim 13, wherein the instructions further cause the device to: selecting, from among the second plurality of traveling points, a traveling point having a second direction other than the dominant direction; and adjusting the second direction of the selected traveling point based on the dominant direction.
 15. The device of claim 8, wherein the unmatched points including at least one of the following: points that are separated from the routes by distances exceeding the first threshold distance, or points having directions that are inconsistent with the route directions; and selecting the candidate transition points from the unmatched points.
 16. The device of claim 15, wherein the instructions further cause the device to: in response to determining that one of the unmatched points is located on one of the at least two plurality of routes, classifying the unmatched point as a traveling point having the route direction of the located route.
 17. A computer program product for tracking the route trajectory of a traveling object being tangibly stored on a non-transient machine-readable medium and comprising machine-executable instructions, the instructions, when executed on a device, causing the device to: obtain positioning data from a positioning device carried on each of a respective at least one traveling object traveling on a plurality of routes; map the positioning data into a plurality of points on a digital map, the digital map including a stored route trajectory along at least two of the plurality of routes, the at least two plurality of routes having different route directions, each point of the plurality of points having a traveling direction indicated by the positioning data, the traveling direction for each point of the plurality of points being saved as metadata; identify points of the plurality of points on the digital map that are matched to the stored route trajectory along the at least two of the plurality of routes based on a distance separating each point from the stored route trajectory along at least two of the plurality of routes being less than a first threshold and having a traveling direction consistent with the route directions of the stored trajectory along the at least two plurality of routes; obtain candidate transition points from the plurality of points on the digital map, the candidate transition points being points that are unmatched to the stored route trajectory along the at least two of the plurality of routes aggregate a plurality of candidate transition points by applying a clustering algorithm to obtain a first cluster of points and a plurality of second clusters of points, the clusters of points being obtained from one of positioning data obtained from a plurality of the traveling objects traveling on the plurality of routes or positioning data obtained from a single object traveling on the plurality of routes multiple times; classifying the first cluster of points as transition points between the at least two plurality of routes or travelling points on one of the plurality of routes, based on verifying a confidence level, the confidence level being verified at least in part based on distances between the first cluster of points and the plurality of second clusters of points; wherein in response to verifying that the confidence level is below a threshold confidence, classifying the first cluster of points as a first plurality of traveling points having a first direction, and in response to verifying that the confidence level is above the threshold confidence, classifying the first cluster of points as the transition points; and automatically adjusting the stored route trajectory at least in part based on the classification of the first cluster of points.
 18. The computer program product of claim 17, wherein the instructions, when executed on the device, cause the device to: select, from among the plurality of second clusters of points, clusters of points such that a distance between each of the selected clusters of points and the first cluster of points is below a second threshold distance; and in response to the number of the selected clusters of points being below a threshold number, determine that the confidence is below the threshold confidence. 